GRI: Graph-based Relative Isomorphism of Word Embedding Spaces
Muhammad Asif Ali, Yan HU, Jianbin Qin, Di Wang
Abstract
Automated construction of bi-lingual dictionaries using monolingual embedding spaces is a core challenge in machine translation. The end performance of these dictionaries relies on the geometric similarity of individual spaces, i.e., their degree of isomorphism. Existing attempts aimed at controlling the relative isomorphism of different spaces fail to incorporate the impact of lexically different but semantically related words in the training objective. To address this, we propose GRI that combines the distributional training objectives with attentive graph convolutions to unanimously consider the impact of lexical variations of semantically similar words required to define/compute the relative isomorphism of multiple spaces. Exper imental evaluation shows that GRI outperforms the existing research by improving the average P@1 by a relative score of upto 63.6%.
BibTeX
@inproceedings{
ali2023gri,
title={{GRI}: Graph-based Relative Isomorphism of Word Embedding Spaces},
author={Muhammad Asif Ali and Yan HU and Jianbin Qin and Di Wang},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=IsDxBXUEd8}
}